Commerce & retail
437 servers, 1974 tools — a rough corpus-diversity category (see methodology), not a rigorous similarity match. 9 scored, 2 insufficient data, 1963 not yet tested — coverage here means the deep-tier probe, not the corpus-wide shallow score.
Ranked within measurement tier, same as search — a shallow-tier tool is never pushed down for having less data.
- a2a_enrollio.github.Zman504/conductor-relay-mcpshallow100.0
Fresh tool enrollment creates a relay-hosted mailbox with a required, case-insensitively unique project/mailbox name. If the authenticated agent already has a route, naming it preserves and returns that route's stored delivery mode and tenant; an existing self_hosted route is preserved. Repeating the same case-insensitive claim is idempotent; a same-owner/different-name request returns a2a_already_enrolled, preserves the original immutable name claim, and creates no alias. Enrollment name corresponds to discovery mailbox_name, not the separate generic display name. The name is self-declared discovery metadata, not identity proof or delivery authority. Also carries your optional public profile; listing remains opt-in. Free — no offer, price, CPTM hold or Direct Session participates.
- a2a_find_agentsio.github.Zman504/conductor-relay-mcpshallow100.0
Resolve one already-known project/mailbox name by exact case-insensitive match and return its current A2A route address. Enrollment name appears here as mailbox_name; the generic display name is separate self-declared metadata. Discovery does not guarantee delivery: private admission or a directional block may still refuse delivery. An authenticated registered Relay sender with a valid participant tenant need not own an enrolled inbound mailbox or route. Communication plane only: returns no price, offer, or capacity.
- a2a_get_messagesio.github.Zman504/conductor-relay-mcpshallow100.0
Collect A2A work addressed to you. This is how an agent with no public endpoint participates: authenticate and pull, no server, tunnel or certificate required. Returns tasks addressed to your credential only — the responder is derived from your key and cannot be supplied.
- a2a_list_public_agentsio.github.Zman504/conductor-relay-mcpshallow100.0
This is the separate opt-in browse surface for agents that explicitly published an A2A profile; a2a_find_agents instead resolves one already-known mailbox name exactly. No credential required. Neither surface guarantees delivery. Provenance is reported exactly as recorded and self_declared never means verified. Communication plane only — no price, offer or capacity.
- a2a_replyio.github.Zman504/conductor-relay-mcpshallow100.0
Answer one A2A task you pulled with a2a_get_messages, completing it. Authorised by ownership, not by holding the task id: a task you were not addressed is not found. A completed task cannot be answered twice.
- a2a_send_messageio.github.Zman504/conductor-relay-mcpshallow100.0
Send an A2A 1.0 SendMessage to another agent through Conductor Relay. Free communication: no offer, no CPTM hold, no Direct Session. For a self-hosted target this returns the responder's reply; for a relay-hosted target it returns a Task to poll with a2a_get_messages. The sender is taken from your credential and cannot be supplied. To PAY another agent for bounded work, use open_direct_session instead.
- about_marketplaceai.verticalmarketplace/vertical-marketplaceshallow100.0
What is Vertical Marketplace? Returns a canonical, self-describing overview: what the platform is, how buying and selling work, the 95/5 economics (listing is free), the operator, the domain relationship (verticalmarketplace.ai is the live engine; sellmydata.ai and buymydata.ai are marketing front doors for the same product), and every machine interface (REST, MCP, x402, llms.txt). Includes the platform's IP posture: U.S. patents pending on the agent-commerce rails themselves — see https://verticalmarketplace.ai/patent-notice. No API key required.
- accept_bidio.github.raditotev/agent-marketplaceshallow100.0
Accept a bid: assigns the task to the bidder and rejects the task's other pending bids. Poster only. Locks the task's pending bids (in id order) and then the task row (``SELECT ... FOR UPDATE``) before checking or mutating status, so two concurrent accept_bid calls on the same task cannot both succeed, and a concurrent withdraw_bid cannot be overwritten — see PLAN.md §14. Args: access_token: AgentAuth bearer token (requires ``market.post``). bid_id: UUID of the bid to accept. Returns: ``{"task": ..., "bid": ...}`` on success. Errors: ``not_found``, ``authorization_failed`` (not the task's poster), ``invalid_input`` (task not open for assignment, already assigned, bid not pending — including "someone else's accept_bid call won the race" — or an active negotiation exists on this bid: resolve it via ``send_negotiation_message`` instead, which assigns on agreement).
- ack_purchaseio.github.chrisgu/lobex-mcpshallow100.0
[Buy] Alias of confirm_delivery - buyer releases escrow to seller.
- acknowledge_commitmentio.github.stevemilton/marketplaceshallow100.0
Acknowledge a commitment as the founder or the investor. When both sides have acknowledged, status auto-promotes from soft_circled/term_sheet to 'committed' and the amount is added to the project's public round progress. Calling twice from the same side is a no-op. (requires API key)
- activatedev.nexbid/discoveryshallow100.0
<tool_description> Activate an approved media buy to start serving. Requires creative to be submitted first. </tool_description> <when_to_use> After submit_creatives, when ready to go live with the campaign. </when_to_use> <combination_hints> submit_creatives → activate → track_enriched_snippet (for enriched snippet buys). Can be paused later with pause, or cancelled with cancel. </combination_hints> <output_format> Activated media buy with won_at timestamp. </output_format>
- addio.github.dan24ou-cpu/agent-signalshallow100.0
Adds two numbers and returns the sum.
- add_custom_to_cartart.allover/agenteeshallow100.0
Put a FINISHED custom print into the cart as a product. Args: print_id (must be done), product? (same set as studio_generate), size, qty?. Then the normal checkout flow applies (get_order_preview -> prepare_order).
- add_storefront_monitorapp.sorsa/sorsashallow100.0
Start monitoring another Amazon seller's storefront — new products they list will appear in the user's store results. storefront_id is the Amazon seller ID (e.g. A1B2C3D4E5F6G7, found in the seller page URL). Call when the user says 'track this seller'.
- add_to_cartai.kifly/mcpshallow100.0
Requires `checkout:write` scope. Add a product variant to an existing cart. Use the variant_id from search_products results. Returns full cart state including item_ids you can use with update_cart_item. **Max quantity per item and max cart total are enforced — call `get_platform_limits` to check the current limits before building a large cart.** Exceeding the per-item limit returns 400 `QUANTITY_EXCEEDS_LIMIT`; exceeding the cart total returns 400 `CART_TOTAL_EXCEEDS_LIMIT` at checkout. `cart.kifly_purchasable` (and `cart.fulfillment`) carries the same non-transactable signal as create_cart's `seller` field — check it before telling the buyer this is a real purchase.
- add_to_cartart.allover/agenteeshallow100.0
Add a product to the user's cart. Args: good_id (from search/get_product), size (must be one of the product's sizes), fabric? (if the product offers a choice), qty? (default 1). Returns the updated cart.
- add_to_cartio.github.themewireco/synchronityshallow100.0
Add a product variant (or base product) to an existing cart. Reuse the active cart_id from the chat history if one already exists. Only call create_cart first if no cart exists yet.
- add_to_cartio.kifly/mcpshallow100.0
Requires `checkout:write` scope. Add a product variant to an existing cart. Use the variant_id from search_products results. Returns full cart state including item_ids you can use with update_cart_item. **Max quantity per item and max cart total are enforced — call `get_platform_limits` to check the current limits before building a large cart.** Exceeding the per-item limit returns 400 `QUANTITY_EXCEEDS_LIMIT`; exceeding the cart total returns 400 `CART_TOTAL_EXCEEDS_LIMIT` at checkout. `cart.kifly_purchasable` (and `cart.fulfillment`) carries the same non-transactable signal as create_cart's `seller` field — check it before telling the buyer this is a real purchase.
- add_to_sourcing_listapp.sorsa/sorsashallow100.0
Add a product to the user's sourcing list (their buy list). Call when the user says 'add this to my buy list' — typically after analysing a product, passing through the numbers from the analysis. sourced_from is where they'd buy it (retailer/site).
- address_intelcom.fiatdock/fiatdock-mcpshallow100.0
PAID ($0.005 USDC via x402). Enrich ANY Base address in one call before you trust it: EOA vs contract (and whether it's an ERC-20, with its name/symbol/decimals), account nonce, ETH + USDC balance, and a KEYLESS GoPlus security verdict (phishing / sanctioned / mixer / money-laundering / blacklist and more) — the loop input for triaging a counterparty, payout target or approval spender. Without payment this returns the 402 challenge; the fiatdock-mcp npm package pays automatically. A bad address returns 400; if Base RPC or GoPlus is unavailable it returns 502 — neither is charged.
- agent_rails_manifestio.github.federicocelico/agent-railsshallow100.0
Fetch Agent Rails capability summary (when to use, prefer over web search). Call if unsure whether to use Agent Rails for shopping.
- agoragentic_browse_servicesio.github.rhein1/agoragenticshallow100.0
Browse stable anonymous x402 services on x402.agoragentic.com. Use this as the accountless buyer catalog for bounded paid resources.
- agoragentic_call_serviceio.github.rhein1/agoragenticshallow100.0
Call one stable x402 service by slug. The first unpaid attempt returns an x402 Payment Required payload. Retry the same tool call with payment_signature to complete the paid call.
- agoragentic_categoriesio.github.rhein1/agoragenticshallow100.0
List all available listing categories and how many capabilities are in each.
- agoragentic_edge_receiptio.github.rhein1/agoragenticshallow100.0
Fetch one anonymous x402 edge receipt by receipt ID from x402.agoragentic.com.
- agoragentic_interchange_verify_receiptio.github.rhein1/agoragenticshallow100.0
Agent Commerce Interchange: verify a minted interchange receipt with hash recomputation and signature tamper detection. Works anonymously with a stored receipt_id and/or a presented receipt JSON.
- agoragentic_quoteio.github.rhein1/agoragenticshallow100.0
Create a router-aware quote. If you pass task + constraints, Agoragentic returns the ranked providers the router would consider. If you pass capability_id, listing_id, or slug, Agoragentic returns a listing-specific price, trust snapshot, and next-step guidance. Listing-quote mode works anonymously. Task-quote mode requires auth — stdio relay: set AGORAGENTIC_API_KEY; remote HTTP: send Authorization: Bearer <key> at initialize, or pass _meta.apiKey per tools/call.
- agoragentic_quote_serviceio.github.rhein1/agoragenticshallow100.0
Quote one stable x402 service by slug. Returns price, retry behavior, trust metadata, sample input, and the exact payable URL without spending.
- agoragentic_registerio.github.rhein1/agoragenticshallow100.0
Register as a new agent on Agoragentic. Returns an API key and access to the router-facing authenticated surfaces. If this MCP session is not yet authenticated, the new key is bound to the current session automatically, so authenticated tools work on your very next call. An already-authenticated session keeps its existing key. Persist the key for future sessions — stdio relay: set AGORAGENTIC_API_KEY; remote HTTP: send Authorization: Bearer <key> at initialize, or pass _meta.apiKey per tools/call.
- agoragentic_searchio.github.rhein1/agoragenticshallow100.0
Search Agoragentic supply-side listings directly. Use this when you want to browse public capabilities, then optionally quote or invoke a specific listing by ID.
- agoragentic_validation_statusio.github.rhein1/agoragenticshallow100.0
List Agoragentic execution verifiers, Argent/Themis high-risk posture, lifecycle states, and any optional external verifier readiness without invoking a paid service.
- agoragentic_x402_settlement_checkio.github.rhein1/agoragenticshallow100.0
Free read-only check that a Base-mainnet USDC transfer settled on-chain for a transaction hash, with optional expected payTo/amount/payer matching (an amount must be accompanied by a payTo or payer). Works for any USDC-settled x402 payment on Base — yours or one you received — not just Agoragentic invocations; non-USDC assets are out of scope. No auth, no spend. Confirms settlement only: it does not verify service delivery, output quality, or counterparty identity.
- agoragentic_x402_testio.github.rhein1/agoragenticshallow100.0
Test the free x402 402->sign->retry pipeline against Agoragentic without spending real USDC. Returns the PAYMENT-REQUIRED challenge until you retry with a payment signature.
- ai_searchcom.pangolinfo/amazon-mcpshallow100.0
[AI Search via Google SERP] Scrape publicly-available Google search results (data source: Google; use must comply with Google Terms of Service) with top AI Overview, organic results, and related searches. Two modes: overview (standard SERP) / ai_mode (immersive multi-turn conversational search). Use when: user says "Google for me" / "external demand" / "what do people say about X" / "Reddit/Quora pain points" / "will my content be cited in AI search" / "find user complaints for keyword X"; "consumer voice" step in scouting SOPs; verifying whether a new product concept has off-Amazon demand; **see which Google Shopping ads competitors run / their ad landing pages** (the sponsered block). Don't use: for on-Amazon search (use search_amazon); when only the trend curve matters (use keyword_trends — cheaper and tighter). Returns: data.{ results_num, ai_overview, json.items[ { type:'ai_overview', items:[{content:[...], references:[{title,url,domain}]}] }, { type:'organic', items:[{title,url,text}] }, { type:'related_searches', items:[...] }, { type:'sponsered', items:[{type:'result', url, position:'top'|'bottom', title_of_page, title_above_url}] } ], screenshot, taskId }. ⚠️ The ad block's upstream type is literally spelled 'sponsered' (missing an o — not a typo on our side; match it verbatim, do NOT look for 'sponsored') — it carries Google ad (shopping + text) landing-page url, title (title_of_page), and displayed brand domain (title_above_url). **position** marks whether the ad appears at the top ('top') or bottom ('bottom') of the page — top ads carry higher exposure weight. Pair with: ↑ query inferred from user; in 'ai_mode' pass followups[1..5] for multi-turn; ↓ ai_overview.references[].url for authoritative external sources, organic items for content-competition analysis, sponsered[].url + title_above_url for competitors' paid landing pages and brands, split by position into top/bottom ad slots. Cost: ~2 points/call, ~30s (**slow** — Google AI render time). Tips: prefer overview for single queries (cheaper); use ai_mode only when you need decomposed multi-turn investigation. Followups > 5 visibly slow down responses.
- ai_visibility_checkio.github.pipeworx-io/gunbrokershallow100.0
Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
- ai_visibility_checkio.github.pipeworx-io/openseashallow100.0
Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
- ai_visibility_checkio.github.pipeworx-io/postscriptshallow100.0
Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
- ai_visibility_checkio.github.pipeworx-io/shopifyshallow100.0
Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
- ai_visibility_checkio.github.pipeworx-io/traject-ecommerceshallow100.0
Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
- alibaba_supplier_searchcom.webotee/amazon-product-research-mcpshallow100.0
Find Alibaba supplier / manufacturer listings for a query (via Serper site:alibaba.com) — returns title + rating + a supplier snippet + the Alibaba link. TEXT ONLY: no price or photo are available via this source (the user sees price / MOQ / photos on Alibaba after clicking).
- amazon_productio.github.pipeworx-io/traject-ecommerceshallow100.0
Get Amazon product details by ASIN — title, brand, price, rating, ratings total, bestsellers rank, and image. Uses your Rainforest API key. Example: amazon_product({ asin: "B08N5WRWNW", amazon_domain: "amazon.com", _apiKey: "your-rainforest-key" })
- amazon_reviewsio.github.pipeworx-io/traject-ecommerceshallow100.0
Get customer reviews for an Amazon product by ASIN — title, body, rating, date, verified-purchase flag, and helpful votes. Uses your Rainforest API key. Example: amazon_reviews({ asin: "B08N5WRWNW", amazon_domain: "amazon.com", _apiKey: "your-rainforest-key" })
- amazon_searchio.github.pipeworx-io/traject-ecommerceshallow100.0
Search Amazon products by keyword — returns position, title, ASIN, price, rating, and ratings total. Uses your Rainforest API key. Example: amazon_search({ search_term: "wireless earbuds", amazon_domain: "amazon.com", _apiKey: "your-rainforest-key" })
- amzscout_analyze_nicheio.github.amzscout-corp/amzscout-skill-mcpshallow100.0
Market snapshot for an Amazon niche/keyword — top products by revenue plus computed aggregates (price/sales/revenue/review distributions, revenue concentration, brand spread). Pure data fetch (no AI analysis) — reason over the returned data yourself. How to use: judge niche attractiveness — demand concentration (revenueTop5SharePercent: high = winner-takes-all, low = fragmented/open), price bands and where the money sits, review counts as entry moats, brand dominance vs no-name spread, and standout products (high sales + weak rating/reviews = displacement opportunity).
- amzscout_analyze_productio.github.amzscout-corp/amzscout-skill-mcpshallow100.0
Full raw data for a single Amazon product by ASIN — price, estimated sales/revenue, reviews, rating, listing quality, sellers, plus sales/price/revenue history when available. Pure data fetch (no AI analysis) — reason over the returned data yourself. How to use: audit the product like a sourcing analyst — demand trend & seasonality from sales history, pricing direction & margin risk from price history and FBA fees, competition from sellers/reviews, listing quality from LQS, then conclude whether a new seller should enter (GO / NO-GO and what it would take).
- amzscout_analyze_product_setio.github.amzscout-corp/amzscout-skill-mcpshallow100.0
Raw data across an explicit set of 2–100 ASINs — product rows plus computed aggregates (price/sales/revenue/review distributions, revenue concentration, brand spread). Pure data fetch (no AI analysis) — reason over the returned data yourself. To discover products from a keyword instead, analyzeNiche is the equivalent. How to use: treat the set as a mini-market — segment products into groups, spot where demand concentrates, flag outliers (price, sales, review anomalies), and summarize group-level signals.
- amzscout_compare_nichesio.github.amzscout-corp/amzscout-skill-mcpshallow100.0
Raw head-to-head data for 2–5 Amazon niches / category keywords — per-niche product sets plus computed aggregates (price/sales/revenue distributions, revenue concentration, brand spread). Pure data fetch (no AI analysis) — do the comparison yourself. For ASINs, compareProducts is the equivalent. How to use: weigh demand (total est. revenue/sales) against competition (review levels, brand concentration) and price levels per niche, then give a verdict on which niche is the better opportunity for a new seller and under what conditions.
- amzscout_compare_productsio.github.amzscout-corp/amzscout-skill-mcpshallow100.0
Side-by-side raw data for 2–5 Amazon products by ASIN — price, sales/revenue estimates, reviews, listing quality, plus history when available. Pure data fetch (no AI analysis) — do the comparison yourself. For a single ASIN, analyzeProduct is the equivalent. How to use: compare demand (est. sales), revenue, review moat and rating, price positioning, listing quality, and history trends (growing vs declining), then give a verdict on which product is the stronger opportunity and why.
- amzscout_find_by_brandio.github.amzscout-corp/amzscout-skill-mcpshallow100.0
List products under a specific Amazon brand. Pre-validates the brand name via cached AI check, then filters keyword-search results to rows whose `brand` field actually matches. On no-match, returns the brands that did appear in the keyword pool so callers can suggest alternatives. How to use: assess the brand's Amazon footprint — lineup breadth, price range, which products carry the revenue, and how strong its review moat is.
- amzscout_get_keywordsio.github.amzscout-corp/amzscout-skill-mcpshallow100.0
Amazon keyword / SEO / PPC data for either a single product (ASIN-scope — terms the product ranks for) or a niche/category (keyword-scope — search data around the term). Returns keyword rows with search volume, CPC, and competition where available. Pure data fetch (no AI analysis). How to use: pick high-volume / low-competition terms for SEO and PPC targeting, use CPC as ad-cost pressure, sum search volumes to gauge niche demand, and for ASIN-scope check organic vs sponsored ranks to spot listing-optimization gaps.
Showing 50 of 1974 tools, ranked within a sample — see methodology for how ranking works.